Yup, David is right, the P-value is for a present/absent call. Thanks to all. but one another question If I have such a data set which I have mentioned earlier,that means 3000 gene and each has single expression level and P-value for a present and absent call and this set up is for all the 39 experimental condition,then can I do any meaningful statistical operation to this data set?

See ?heatmap for details and customisation. To read you data into R, see ?read.table, assuming it is in a csv format, or similar.

Hope this helps.

EDIT: The dendrograms have been added automatically. In brief, the heatmap represents the values in your data matrix (scaled and centred by default) and the hierarchical clustering is performed along columns and rows using the hclust function (see ?hclust) based on eucledian distance (see ?dist).

Has R put the trees in or is that external? Cant see how it could be done from your inputs. Only Ive been looking to do a heatmap just like that AND to learn R for ages and this would kill two birds with one stone!

suppose that i have already downloaded GSE63706 and normalized that and i have a normalized text file now. and i have also a list of probsets (a text file of my interest probsets) in this array...i want to have a heat map showing the expression pattern of my interest probsets in this array, for example in this array i have 4 varieties and different tissues (rind and flesh) and phases (0,10,20,30,40 and 50 days after harvesting). heat maps showing the expression pattern of my probsets in varieties, tissues and phases i mean

If you are a Biomedical researcher I would recommend Gene-E from the Broad Institute to generate heatmaps, calculate differential gene expression and clusterings, and GenePattern (also from the Broad) to to perform more advanced analyses such as GSEA.

If you have Affymetrix .CEL files and an excel spreadsheet with clinical annotations you can use InSilico DB (https://insilicodb.com) to go from you .CEL files to a list of differential expressed genes in Gene-E in ~20 mintutes (registration and upload included). Large datasets take longer to upload.

There are miRNA sets containing cancer and healthy samples. I want to identify miRNA probes that differentially expressed in healthy subjects and cancer patients by applying t-test. Is there any one can help?